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arXiv · 2106.09896

Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations

Lingzhi Wang·Jing Li·Xingshan Zeng·Haisong Zhang·Kam-Fai Wong
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Abstract

Quotations are crucial for successful explanations and persuasions in interpersonal communications. However, finding what to quote in a conversation is challenging for both humans and machines. This work studies automatic quotation generation in an online conversation and explores how language consistency affects whether a quotation fits the given context. Here, we capture the contextual consistency of a quotation in terms of latent topics, interactions with the dialogue history, and coherence to the query turn's existing content. Further, an encoder-decoder neural framework is employed to continue the context with a quotation via language generation. Experiment results on two large-scale datasets in English and Chinese demonstrate that our quotation generation model outperforms the state-of-the-art models. Further analysis shows that topic, interaction, and query consistency are all helpful to learn how to quote in online conversations.

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Lingzhi Wang, Jing Li, Xingshan Zeng, Haisong Zhang, Kam-Fai Wong. 2021-06-18. Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations. https://arxiv.org/abs/2106.09896

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